We train our EBMs using the persistent initialization described above in tandem with a pretrained generator on both CIFAR-10 and ImageNet. We use the same SNGAN models as before for our CIFAR-10 experiments, with the exception that the generator is pretrained instead of learned. For our ImageNet experiments, we use the BigGAN [1]} discriminator architecture for our EBM modified for input size \(224\times 224\)  and a pretrained BigGAN Generator. Our naturally trained classifier \(f(x)\)  is a pretrained WideResNet 28-10 [2]} for CIFAR-10 and a pretrained EfficientNetB-7 architecture [3]} for ImageNet.
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